Statistical analysis with missing data
Statistical analysis with missing data
Mixture model clustering for mixed data with missing information
Computational Statistics & Data Analysis
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Learn++.MF: A random subspace approach for the missing feature problem
Pattern Recognition
Classifying patterns with missing values using Multi-Task Learning perceptrons
Expert Systems with Applications: An International Journal
Mixtures of common factor analyzers for high-dimensional data with missing information
Journal of Multivariate Analysis
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Imputation is a widely used method for handling missing data. It consists in the replacement of missing values with plausible ones. Parametric and nonparametric techniques are generally adopted for modelling incomplete data. Both of them have advantages and drawbacks. Parametric techniques are parsimonious but depend on the model assumed, while nonparametric techniques are more flexible but require a high amount of observations. The use of finite mixture of multivariate Gaussian distributions for handling missing data is proposed. The main reason is that it allows to control the trade-off between parsimony and flexibility. An experimental comparison with the widely used imputation nearest neighbour donor is illustrated.